Policy Similarity

Policy similarity, a burgeoning area of research, focuses on quantifying and leveraging the resemblance between different policies, particularly within reinforcement learning and multi-agent systems. Current research explores methods for measuring policy distance, using techniques like conditional representation learning and SHAP explanations, to improve algorithm performance, enhance diversity in multi-agent settings, and enable efficient policy distillation. This work has implications for improving the robustness and interpretability of AI systems, particularly in scenarios requiring zero-shot coordination or dealing with limited data, and offers valuable insights into the structure and behavior of complex policies.